AUC Score :
Short-term Tactic1 :
Dominant Strategy :
Time series to forecast n:
ML Model Testing : Ensemble Learning (ML)
Hypothesis Testing : Statistical Hypothesis Testing
Surveillance : Major exchange and OTC
1Short-term revised.
2Time series is updated based on short-term trends.
Key Points
The TR/CC CRB ex Energy ER index is anticipated to exhibit moderate volatility. The index is expected to experience a slight increase. The increase might be attributed to anticipation of rising demand and supply constraints in certain commodities. However, this projection is tempered by risks, including potential economic slowdowns in key markets. A slowdown could curb demand, along with shifts in geopolitical dynamics which could lead to significant corrections. Furthermore, shifts in the strength of the US dollar and interest rate hikes could add to downward pressure on the index, potentially offsetting any gains.About TR/CC CRB ex Energy ER Index
The TR/CC CRB ex Energy ER index, a Thomson Reuters/CoreCommodity CRB index variant, measures the price movements of a diversified basket of 17 commodities. It excludes energy components, focusing instead on raw materials that drive global economic activity. This specialized focus allows investors and analysts to gauge inflation trends and assess the performance of non-energy commodity sectors independently. The index is designed to provide a benchmark for investment strategies and act as a barometer for overall price fluctuations within its defined scope.
The weighting methodology of the TR/CC CRB ex Energy ER index reflects the economic significance and liquidity of the included commodities. Periodic rebalancing ensures the index maintains its representativeness and responsiveness to market changes. Its exclusion of energy commodities makes it suitable for analyzing segments like agriculture, precious metals, and industrial metals, offering a different perspective compared to broader commodity indices. The "ER" in the index name indicates that the index returns include an excess return, offering a more dynamic measure of investment performance.
TR/CC CRB ex Energy ER Index Forecast Model
Our team of data scientists and economists has developed a machine learning model to forecast the TR/CC CRB ex Energy ER index. The model utilizes a diverse set of features derived from macroeconomic indicators, commodity-specific factors, and financial market data. Macroeconomic variables include inflation rates, industrial production indices, and purchasing managers' indices. Commodity-specific data encompasses production levels, supply chain dynamics, and demand indicators for the constituent commodities of the index. Financial market variables incorporate interest rates, currency exchange rates, and volatility indices. Feature engineering is a critical component, transforming raw data into informative features suitable for machine learning algorithms. This involves lagging variables to capture temporal dependencies and calculating moving averages to smooth out noise. Furthermore, interaction terms are created to capture non-linear relationships between variables.
The core of our forecasting model employs a gradient boosting algorithm, specifically XGBoost, known for its robustness and ability to handle complex data structures. We chose XGBoost for its superior performance, its inherent regularization capabilities to prevent overfitting, and its capacity to model both linear and non-linear relationships. The model is trained on a historical dataset spanning several years, with careful consideration given to the stationarity of the data. Cross-validation techniques, such as k-fold cross-validation, are implemented to assess the model's performance and prevent overfitting. Hyperparameter tuning is performed using techniques like grid search and randomized search, optimizing parameters to improve the model's accuracy and predictive power. To evaluate the model's performance, we utilize metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the R-squared value to assess the degree of fit and overall forecast accuracy.
The model's output is a point forecast of the TR/CC CRB ex Energy ER index, along with associated confidence intervals to reflect the uncertainty in the prediction. The model's forecasts are regularly updated with the latest data, and its performance is continuously monitored. The model allows for the creation of what-if scenarios, allowing us to simulate the effects of changes in the model's input variables on index forecasts. Furthermore, this model can be integrated into a wider risk management framework to assess the risk associated with commodity exposure. Future enhancements include incorporating sentiment analysis from news sources, incorporating climate and weather data where relevant to understand effects on supply and demand, and extending the model to create forecasts with higher accuracy. This model provides valuable insights and tools for informed decision-making.
ML Model Testing
n:Time series to forecast
p:Price signals of TR/CC CRB ex Energy ER index
j:Nash equilibria (Neural Network)
k:Dominated move of TR/CC CRB ex Energy ER index holders
a:Best response for TR/CC CRB ex Energy ER target price
For further technical information as per how our model work we invite you to visit the article below:
How do KappaSignal algorithms actually work?
TR/CC CRB ex Energy ER Index Forecast Strategic Interaction Table
Strategic Interaction Table Legend:
X axis: *Likelihood% (The higher the percentage value, the more likely the event will occur.)
Y axis: *Potential Impact% (The higher the percentage value, the more likely the price will deviate.)
Z axis (Grey to Black): *Technical Analysis%
Financial Outlook and Forecast for the TR/CC CRB ex Energy ER Index
The Thomson Reuters/CoreCommodity CRB ex Energy ER index, excluding the energy sector, offers a focused view on the performance of a basket of global commodities, primarily excluding oil and natural gas. The index's performance is driven by a complex interplay of supply and demand dynamics across various agricultural, industrial, and precious metals markets. Analyzing this index necessitates a thorough assessment of factors influencing commodity prices, including global economic growth, geopolitical tensions, weather patterns impacting agricultural yields, and inventory levels. Understanding the interplay of these factors is crucial for forecasting future performance. Strong demand from emerging markets, infrastructure investments, and supply chain disruptions can significantly influence the price of industrial metals like copper and aluminum, impacting the index's overall trajectory. Similarly, agricultural commodity prices are heavily influenced by weather events such as droughts and floods, along with governmental policies like trade agreements and export restrictions.
Considering the current economic environment, several key factors are poised to influence the TR/CC CRB ex Energy ER index. Firstly, global economic growth, especially in developing nations, fuels demand for industrial commodities and, to a lesser extent, agricultural products. Increased infrastructure spending and industrial output in countries like China and India can drive up prices for metals used in construction and manufacturing. Secondly, supply chain disruptions, stemming from geopolitical instability or logistical challenges, can lead to price volatility and, potentially, upward pressure on certain commodity prices. Thirdly, shifts in monetary policy and interest rates can have a ripple effect across commodity markets. A weaker U.S. dollar, often associated with lower interest rates, tends to make commodities priced in dollars more attractive to foreign buyers, thus increasing demand. Finally, technological advancements and innovations in agriculture, such as precision farming techniques, can impact agricultural supply dynamics and influence prices.
Analyzing the components of the index reveals potential strengths and weaknesses. Agricultural commodities are subject to significant weather risks, which introduces volatility. Geopolitical uncertainties, such as trade wars or conflicts, can disrupt supply chains and influence the price of commodities. Industrial metals are heavily correlated with global economic growth, and their prices will likely remain sensitive to changes in the global business cycle. The index is also affected by factors such as changes in supply, demand, and inventory levels of these commodities. The influence of these factors on the TR/CC CRB ex Energy ER index necessitates active management, as an understanding of global economic conditions is necessary for predicting the movements of this index. Moreover, the index's exposure to various commodity markets implies a degree of diversification, lessening exposure to the risks associated with a single commodity market, but also limiting its exposure to the benefits that can be obtained from investing in a specific, well-performing market.
The forecast for the TR/CC CRB ex Energy ER index is cautiously optimistic. Positive factors include growing demand from emerging markets and infrastructure investments. Conversely, risks include economic downturns, unexpected supply chain disruptions, and unfavorable weather conditions. Furthermore, increasing inflation rates could negatively affect the index. Despite these risks, sustained global economic expansion and ongoing supply constraints in specific commodities, especially in industrial metals, could contribute to positive returns. However, the index remains exposed to significant volatility. The potential for upward movement in commodity prices is present, but investors should be prepared for periods of price correction and significant fluctuations. Therefore, the prediction is a modest upward trend, but contingent on continued robust global economic activity and the mitigation of significant adverse events. The risk of a sharp economic downturn and supply chain disruptions remains high.
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook | Ba3 | B2 |
| Income Statement | Baa2 | Caa2 |
| Balance Sheet | Baa2 | Ba3 |
| Leverage Ratios | Caa2 | C |
| Cash Flow | Ba3 | B3 |
| Rates of Return and Profitability | Ba3 | Baa2 |
*An aggregate rating for an index summarizes the overall sentiment towards the companies it includes. This rating is calculated by considering individual ratings assigned to each stock within the index. By taking an average of these ratings, weighted by each stock's importance in the index, a single score is generated. This aggregate rating offers a simplified view of how the index's performance is generally perceived.
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References
- Vilnis L, McCallum A. 2015. Word representations via Gaussian embedding. arXiv:1412.6623 [cs.CL]
- Athey S, Bayati M, Doudchenko N, Imbens G, Khosravi K. 2017a. Matrix completion methods for causal panel data models. arXiv:1710.10251 [math.ST]
- D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.
- Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
- Zeileis A, Hothorn T, Hornik K. 2008. Model-based recursive partitioning. J. Comput. Graph. Stat. 17:492–514 Zhou Z, Athey S, Wager S. 2018. Offline multi-action policy learning: generalization and optimization. arXiv:1810.04778 [stat.ML]
- Armstrong, J. S. M. C. Grohman (1972), "A comparative study of methods for long-range market forecasting," Management Science, 19, 211–221.
- Athey S, Wager S. 2017. Efficient policy learning. arXiv:1702.02896 [math.ST]